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Problematic Cost–Utility Analysis of Interventions for Behavior Problems in Children and Adolescents

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Problematic Cost–Utility Analysis of Interventions for Behavior Problems in Children and Adolescents DOI: 10.1002/cad.20360 REVIEW Problematic cost–utility analysis of interventions for behavior problems in children and adolescents Marinus H. van IJzendoorn1,2 Marian J. Bakermans-Kranenburg3 1 Erasmus University Rotterdam, Rotterdam, The Netherlands Abstract 2 University of Cambridge, Cambridge, UK Cost–utility analyses are slowly becoming part of randomized control trials evaluating physical and 3 Vrije Universiteit Amsterdam, Amsterdam, The Netherlands mental health treatments and (preventive) inter- ventions in child and adolescent development. The Correspondence British National Institute of Health and Care Excel- Marinus H. van IJzendoorn, Erasmus Univer- lence, for example, insists on the use of gains in sity,Rotterdam, Netherlands. Email: [email protected] Quality Adjusted Life Years (QALYs) to compute the “value for money” of interventions. But what counts Funding information as a gain in quality of life? For one of the most widely The European Research Council; the Dutch used instruments, the EuroQol 5 Dimensions scale Ministry of Education, Culture, and Science; (EQ-5D), QALYs are estimated by healthy individ- the Netherlands Organization for Scientific Research (NWO grant number 024.001.003) uals who provide utility scores for specific health states, assuming that the best life is a life without self-experienced problems in five domains: mobil- ity, self-care, usual activities, pain/discomfort, and anxiety/depression. The worst imaginable outcome is defined as “a lot of problems” in each of these five domains. The impact of the individual’sproblems on the social network is not weighted, and important social–developmental domains (externalizing prob- lems, social competence) are missing. Current cost– utility computations based on EQ-5D favor physi- cal health over mental health, and they rely on adult weights for child and adolescent quality of life. Thus, a level playing field is absent, and developmental expertise is sorely missing. KEYWORDS cost-utility analysis, interventions, children, mental health, Qual- ity Adjusted Life-Years (QALY), EuroQol 5 Dimensions scale (EQ- 5D), critical review This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 Wiley Periodicals LLC Child & Adolescent Development. 2020;2020:89–102. wileyonlinelibrary.com/journal/cad 89 90 van IJZENDOORN and BAKERMANS-KRANENBURG For policy advisers and politicians, health economics is increasingly important because of cost–utility analyses of randomized control trials (RCTs) on mental health problems. A cru- cial question is whether and when they are willing to pay the price of investment in child and adolescent interventions. In particular, we focus here on the costs of interventions aiming at behavior problems and lack of social competence in return for benefits enjoyed by children, their parents, and society. In its report on “Judging whether public health inter- ventions offer value for money” the National Institute of Health and Care Excellence (NICE, 2018) in the United Kingdom, for example, recommends the use of Quality Adjusted Life Years or QALYs to compute the value for money of interventions. In health economics, other approaches have been developed to estimate costs and benefits of interventions, and the willingness to pay for a treatment, for example, the Disability Adjusted Life Year or DALY approach (e.g., McBain et al., 2016) but they are beyond the scope of the current paper (see Drummond, Sculpher, Claxton, Stoddart, & Torrance, 2015, for other methods). In this paper, we discuss the scientific, normative, and developmental assumptions of one of the most widely used health economics models as currently applied to child and adoles- cent (preventive) intervention programs. The EuroQol 5 Dimensions scale (EQ-5D) used to retrieve the utility scores to compute QALYs is taken as an example to illustrate the chal- lenges of a predominant health economics approach. Health economics is an emerging field of inquiry that cannot be left to economists alone. Developmental science expertise should be brought to bear on the premises, measurements, and analyses in cost–utility computations, lest children, adolescents, and their families suffering from mental health issues pay the price. 1 COST–UTILITY ANALYSIS Cost–utility analysis is often defined as the evaluation of “the impact of the intervention in terms of improvements in preference-weighted health-related quality of life, such as the Quality Adjusted Life Year (QALY)” (Beecham, 2014; p. 715). A basic idea is that it is possible to create one common yardstick or generic currency across all health-related interventions. The QALY would be such a currency: the gold standard against which any health-related intervention in any clinical or at-risk group could be measured, allowing decision-makers to select the prevention or intervention program with the best cost/quality ratio. Based on QALY, it would even be possible to weight the best value for money of medical treatments against that of mental health interventions, a truly interdisciplinary ambition. The idea behind QALY was introduced in a paper by Klarman, Francis, and Rosenthal (1968) on the advantages of transplantation versus dialysis in patients with renal failure (see also MacKillop & Sheard, 2018). In this paper Klarman not only took into account the number of years of life added by each of the treatments, but also the quality of these extra years of life after transplantation, which was estimated to be 25% better than with dialysis. In QALY, the combination of quantity (mortality) and quality (morbidity) of life years is the core of the computation. It is evident that patients experience life after transplantation as “better,” compared to life with regular dialysis. But how did Klarman et al. (1968) arrive at the 25% quality bonus? In this case, they just assumed without any further theoretical or empirical evidence that the extra life quality could be quantified as one quarter of each life year gained. 1.1 Creating a gold standard for quality of life A more sophisticated way to estimate quality of life has been developed by assessing patients’ or non-patients’ preferences for a shorter life without health issues, versus a van IJZENDOORN and BAKERMANS-KRANENBURG 91 TABLE 1 Time trade-off (TTO) weights for the various health states represented by EuroQol 5 Dimensions Scale (EQ-5D) to compute quality adjusted life years (QALYs) Mobility Self-care Activity Pain Depression Some problems .069 .104 .036 .123 .071 A lot of problems .314 .214 .094 .386 .236 Constant .081; malus for “a lot of problems” −.269. Weights derived from Dolan et al. (1995). longer life with health problems lowering the quality of the extra years. The preferences are established on the basis of a generic measure of quality of life that covers various domains of functioning such as physical, mental, and social functioning. For example, the widely used instrument EQ-5D (see EuroQol, 2020) comprises five questions, covering five dimensions, that is, physical mobility, looking after yourself, doing usual activities, having pain or discomfort, and feeling anxious or depressed. In the EQ-5D-Y (Youth version; see EuroQol, 2020), the questions have been slightly adapted, using, for example, the terms worried, sad, or unhappy instead of anxious or depressed. Each dimension has three lev- els corresponding to (1) “no problems,” (2) “some problems,” and (3) “a lot of problems.” The various health states are indicated by one of the three scores on the five dimensions (e.g., if the respondent indicates to experience a lot of problems in the first dimension, mobility, and some depressive problems but no problems in the other dimensions, the health state is represented as 31112. In an updated measure, the EQ-5D-5L (see EuroQol, 2020), the same five dimensions are used with more differentiated response alternatives (no, slight, moderate, severe, and extreme). Validation of this revized measure is under- way but, currently, NICE still recommends the three-level utility estimates as a basis for computing QALYs. One way to establish preferences or utilities for each of the various health states is to sur- vey a large representative sample asking the respondents to choose between extra life years with the impairments in each of these dimensions (t) and a shorter life without any prob- lems on these dimensions (x). This time trade-off (TTO) approach results in a preference or utility score for the impaired status of x/t. In other words, the respondents are asked to indi- cate the number of years in full health that they consider equivalent to 10 years in a specific impaired state (e.g., with a lot of problems in the mobility dimension and some problems with depression, state 31112). When a respondent considers 6 years in full health equiva- lent to 10 years with a lot of mobility problems and some depressive problems, the respon- dent’s weight for this state would be 6/10. Based on a sample of respondents answering the same question their average opinion is the weight for that state of (ill) health which sub- sequently is used to compute the QALY. The approach fits the traditional economic model of the “homo economicus” who is a rational respondent with preferences (or utilities) with the goal to maximize these individual, self-interested preferences (Melé & Cantón, 2014). 1.2 From full health to death In fact, the weights are rankings on a continuum from zero (death; state 33333) to one (full health; state 11111). In a UK representative sample of 2,997 non-clinical adult par- ticipants, Dolan, Gudex, Kind, and Williams (1995) used the TTO approach to valuate the various states. The participants showed their preferences (utilities) for a large number of states, which resulted in coefficients for levels 2 (some problems) and 3 (a lot of problems), respectively, for each of the domains, see Table 1.
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